Untitled Quiz
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Untitled Quiz

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Questions and Answers

A vector is linearly dependent if it is equal to the zero vector.

True

Two vectors are linearly dependent if one is a multiple of the other.

True

If you perform Gaussian elimination and find a line with all zeros, it indicates linear dependence.

True

What is the relationship between linear mappings and linear independence?

<p>Linear mappings preserve linear independence if the map is injective.</p> Signup and view all the answers

Can a subset of independent vectors generate a vector space?

<p>No, it cannot.</p> Signup and view all the answers

A vector space V is finitely generated if there exists an n and a surjective mapping T: F^n → W.

<p>True</p> Signup and view all the answers

What defines a base of a vector space?

<p>A base consists of independent and generating vectors.</p> Signup and view all the answers

What does 'maximal-independent' mean for a base?

<p>No additional vector can be added without losing independence.</p> Signup and view all the answers

Bijective linear mappings bring bases into bases.

<p>True</p> Signup and view all the answers

In a space V of dimension n, which of the following statements is true?

<p>All of the above.</p> Signup and view all the answers

What does it mean for two vector spaces V and W to be isomorphic?

<p>V is isomorphic to W if dimV = dimW.</p> Signup and view all the answers

More than one linear mapping can send a basis of an n-dimensional vector space V to any n vectors in another vector space W.

<p>False</p> Signup and view all the answers

What does the dual space V' consist of?

<p>It is the set of all linear forms on V.</p> Signup and view all the answers

What is the basis of the dual space?

<p>The dual basis consists of functions that map coordinates.</p> Signup and view all the answers

What is the transposition of a linear mapping?

<p>It is a function that creates a linear form on V from a linear form on W by composition.</p> Signup and view all the answers

What is the annihilator of a subspace?

<p>It is the set of all linear forms where f(x) = 0 for all x in M.</p> Signup and view all the answers

The _____ of the transpose is the annihilator of the _____

<p>kernel, image</p> Signup and view all the answers

Complete the theorem: If T: V → W is a linear mapping, then T is surjective ⇔ T' is ______.

<p>injective</p> Signup and view all the answers

What is the bidual of V?

<p>V''</p> Signup and view all the answers

Why is the canonical embedding important?

<p>It builds an isomorphism between V and V''.</p> Signup and view all the answers

What's an operation?

<p>0 holds: a#b is in A</p> Signup and view all the answers

What's a semigroup?

<p>Property 0: a#b is in a; Property 1: (a#b)#c = a#(b#c)</p> Signup and view all the answers

What's a monoid?

<p>Property 0: a#b is in a; Property 1: (a#b)#c = a#(b#c); Property 2: It exists a unit such that: a#u=a</p> Signup and view all the answers

What's a group?

<p>Property 0: a#b is in a; Property 1: (a#b)#c = a#(b#c); Property 2: It exists a unit such that: a#u=a; Property 3: It exists an inverse such that: a#ã=u</p> Signup and view all the answers

What's an abelian group?

<p>Property 0: a#b is in A; Property 1: (a#b)#c = a#(b#c); Property 2: It exists a unit such that: a#u=a; Property 3: It exists an inverse such that: a#ã=u; Property 4: a#b = b#a</p> Signup and view all the answers

What's a field? Give some examples.

<p>F is a field if (F, +) is an abelian group and (F*, *) is an abelian group. Examples of fields are: B, Q, R, C.</p> Signup and view all the answers

What does it mean that b is divisible by a?

<p>Means that we can write it as b = aq for some q in the integral domain D. We write a | b.</p> Signup and view all the answers

Prove that the remainder and the quotient are unique.

<p>If a = bq + r = bq' + r', then r - r' = b(q' - q) implies r = r' and q = q'.</p> Signup and view all the answers

What does it mean that a ~ b (mod m)? Prove it.

<p>It means that m | (a - b). It means that (a - b) = mk for some k in Z.</p> Signup and view all the answers

What's an equivalence relation?

<p>A relation that is reflexive, symmetric, and transitive.</p> Signup and view all the answers

What is a relation? Give examples.

<p>A relation is a subset of pairs of elements from a set. For example, a &gt;= b is a relation.</p> Signup and view all the answers

Prove that any non-empty set of integers closed under addition and subtraction either consists of zero alone or else contains a least positive element.

<p>If the set is not empty, it exists at least one element a. The process finds all multiples of a.</p> Signup and view all the answers

What is a vector?

<p>A vector can be considered as a function that associates a certain number of slots with a certain number of values of the field.</p> Signup and view all the answers

How are operations between vectors defined?

<p>They are defined point-wise, such as scalar multiplication and sum.</p> Signup and view all the answers

When is a subset of F^S said to be linear?

<p>Whenever all possible linear combinations in that subset are still in the subset.</p> Signup and view all the answers

What is a vector space?

<p>V is a vector space over a field F if it satisfies properties like closure under addition and scalar multiplication.</p> Signup and view all the answers

Can you mention some examples of vector spaces?

<p>Some examples are: continuous functions, polynomials, vectors in R^2 and R^3.</p> Signup and view all the answers

Prove that the zero of a vector space is unique.

<p>If k + x = x, then by canceling x we find k = 0 for every x.</p> Signup and view all the answers

Define linear combination.

<p>A vector x is a linear combination of vectors (x1, ..., xn) if x = c1x1 + ... + cnxn.</p> Signup and view all the answers

What is a linear subspace of a vector space?

<p>A vector linear subspace M satisfies properties like closure under addition and scalar multiplication.</p> Signup and view all the answers

Is a subspace always a vector space itself? What is the smallest vector linear subspace you know?

<p>Yes, it is. The smallest vector space is the trivial vector space which consists of the zero vector.</p> Signup and view all the answers

Given two subspaces M and N, define M+N and M ∩ N.

<p>M+N = {x+y: x in M and y in N}; M ∩ N = {x in V: x in M and x in N}.</p> Signup and view all the answers

What is a linear mapping?

<p>A mapping T:V→W is linear if T(x+y) = Tx + Ty and T(cx) = cT(x).</p> Signup and view all the answers

What is the definition of linear form?

<p>A linear form is a linear mapping f:V→F, where F is regarded as a vector space over itself.</p> Signup and view all the answers

Prove that given two subspaces M and N and a linear mapping T:V→W, T(M) and T(N) are both subspaces.

<p>T(0) = 0. For any y, y' in T(M), T(y+y') = Ty + Ty'.</p> Signup and view all the answers

Define the kernel and the image of a linear mapping T.

<p>The kernel is KerT = {x in V : Tx = 0}; the image is ImT = {Tx : x in V}.</p> Signup and view all the answers

What properties do you get from the KerT?

<p>For x, x' in V, Tx = Tx' IFF x - x' in KerT; KerT = {0} IFF T is injective.</p> Signup and view all the answers

If V and W are vector spaces over F, define the operations of L(V, W) and prove it.

<p>Define sum as (S+T)x = Sx + Tx and scalar multiplication as (aT)x = a(Tx).</p> Signup and view all the answers

What does it mean that V is isomorphic to W?

<p>It means there exists a bijection T: V→W.</p> Signup and view all the answers

Prove that being isomorphic is an equivalence relation.

<p>Reflexivity: identity mapping; Symmetry: inverse mapping; Transitivity: composition of bijections.</p> Signup and view all the answers

What is an equivalence relation in a set X?

<p>A relation that satisfies reflexivity, symmetry, and transitivity.</p> Signup and view all the answers

Give at least three fundamental examples of equivalence classes.

<ol> <li>f:A→B having the same image; 2. x - y in M for vectors; 3. Cosets of vector subspaces.</li> </ol> Signup and view all the answers

What is a partition? What are its properties?

<p>A partition is a set of non-empty subsets where each element belongs to exactly one subset.</p> Signup and view all the answers

Define quotient set and quotient mapping.

<p>The quotient set X/~ contains all equivalence classes; the quotient mapping assigns every x to its equivalence class.</p> Signup and view all the answers

What is the quotient vector space?

<p>Given V and a subspace M, V/M is defined using sum and scalar multiplication from V.</p> Signup and view all the answers

What does the first isomorphism theorem say?

<p>N is a linear subspace of V; T(V) is a linear subspace of W; V/KerT is isomorphic to Im(V).</p> Signup and view all the answers

How is linear span defined? What are its characteristics?

<p>The linear span of a set A is the set of all possible linear combinations of its elements.</p> Signup and view all the answers

Prove that if T:V→W is linear and A is a subset of V, then T([A]) = [T(A)].

<p>T([A]) = T(c1x1 + ... + cnxn) = c1T(x1) + ... + cnT(xn) = [T(A)].</p> Signup and view all the answers

Prove that surjective linear mappings bring generators into generators.

<p>If the map is surjective, T(A) generates W, implying T(A) is generating.</p> Signup and view all the answers

What does it mean that a finite list of vectors is linearly dependent?

<p>A list of n vectors is dependent if coefficients exist such that c1x1 + ... + cnxn = 0, with at least one non-zero.</p> Signup and view all the answers

What can you say about a map T:F^n→W that is defined by T(a1, a2, ..., an) → a1x1 + ... + anxn when the chosen {x1, x2, ..., xn} are linearly dependent?

<p>T is not injective, as there exists coefficients making T equal to zero.</p> Signup and view all the answers

When can you quickly recognize that a list of vectors is linearly dependent?

<p>If there exists a linear combination of them that gives you the zero vector with non-trivial coefficients.</p> Signup and view all the answers

Study Notes

Operations in Algebra

  • An operation is defined as a function where for any elements a and b from a set A, the result a # b is also in A.

Algebraic Structures

  • A semigroup requires closure under an operation and associativity: (a # b) # c = a # (b # c).
  • A monoid adds the existence of an identity element, u, such that a # u = a.
  • A group extends a monoid with the necessity of inverses: for every element a, there is an element ã such that a # ã = u.
  • An abelian group is a group where the operation is commutative: a # b = b # a.

Fields and Divisibility

  • A field requires two operations (+ and ·), with both structures being abelian groups and the existence of multiplicative inverses.
  • Examples of fields include B (binary), Q (rational numbers), R (real numbers), and C (complex numbers).
  • Divisibility means b can be expressed as b = aq for some q in the integers, denoted a | b.

Modular Arithmetic

  • Congruence a ~ b (mod m) means m divides (a - b).
  • It implies that a and b share the same remainder when divided by m.

Relations and Equivalence

  • An equivalence relation is reflexive, symmetric, and transitive.
  • A relation on a set L consists of ordered pairs from L x L.
  • The equivalence class [x] includes all elements that are equivalent to x under a given relation.

Vector Spaces

  • A vector can be viewed as a function mapping indices to field values, defined in F^S.
  • Vector operations are pointwise: scalar multiplication and vector addition.
  • A linear subspace respects the closed nature of vector operations and contains the zero vector.

Properties of Vector Spaces

  • A vector space must satisfy properties including closure under addition and scalar multiplication, associativity, commutativity, existence of additive identity and inverses.
  • A linear combination involves scalars combining vectors to form another vector in the same space.

Linear Mappings

  • A linear mapping T: V → W requires T(x + y) = T(x) + T(y) and T(cx) = cT(x).
  • Kernel is the set of vectors that map to zero, while image refers to the range of mappings.

Span and Independence

  • The span of a set A includes all linear combinations, forming the smallest subspace containing A.
  • Vectors are linearly dependent if a non-trivial combination of them equals zero; otherwise, they are independent.
  • A collection of vectors can generate a vector space if they span it.

Quotient Spaces and Isomorphisms

  • A quotient vector space V/M is formed by defining operations using representatives from equivalence classes.
  • Isomorphism between V and W indicates a bijective mapping preserving structure; two spaces are isomorphic if such a mapping exists.

Fundamental Definitions

  • Partition divides a set into non-empty, disjoint subsets, with every element belonging to one subset.
  • The first isomorphism theorem relates the kernel and image of a linear mapping, linking the quotient of vector spaces.

Bases and Dimension

  • A basis of a vector space is a set of vectors that are independent and generate the space, and a canonical basis uses standard unit vectors.
  • The mapping of dimensions links finite generation to the existence of surjective mappings onto vector spaces.

Maximal Independence and Minimal Generating Sets

  • A basis is maximal independent when adding any new vector to the basis makes it dependent.
  • It is minimally generating if removing any vector from the basis causes the set to no longer generate the space.### Linear Independence and Bases
  • Adding another vector ( k ) to a list of vectors ( {x_1, x_2, \ldots, x_n} \ causes it to lose linear independence.
  • Removing the last vector ( x_n ) from a list results in a set ( {x_1, x_2, \ldots, x_{n-1}} ) that does not generate the entire space.

Bijective Linear Mappings

  • Bijective linear mappings ( T: V \to W ) transform bases in vector space ( V ) into bases in vector space ( W ).
  • Injective (one-to-one) mappings preserve linear independence among vectors.
  • Surjective (onto) mappings ensure that generating sets stay generating.

Properties of Independent and Generating Lists

  • In a vector space ( V ) of dimension ( n ):
    • An independent list of vectors can have a maximum length of ( n ).
    • A generating list must have at least ( n ) vectors.
    • Lists that are independent or generating, and are exactly of length ( n ), are bases.

Isomorphism and Dimension

  • Two vector spaces ( V ) and ( W ) are isomorphic if and only if ( \text{dim } V = \text{dim } W ).
  • If the spaces are isomorphic, they have a linear bijection, ensuring equal dimension.

Uniqueness of Linear Mappings

  • A linear mapping that sends a basis from an ( n )-dimensional space ( V ) to any ( n ) vectors in ( W ) is unique.
  • It is possible to extend a mapping by completing a basis, mapping additional elements to zero.

Dual Spaces

  • The dual space ( V' ) consists of all linear forms ( L(V, F) ) and has a dimension equal to that of ( V ).
  • No natural isomorphism exists between the vector space and its dual space.

Dual Basis

  • The dual basis for ( (F^n)' ) is composed of functions ( f_i ) such that ( f_i(a_1, \ldots, a_n) = a_i ), where each function corresponds to an individual coordinate.

Transpose of Linear Mappings

  • For a linear mapping ( T: V \to W ), the transpose ( T' ) is defined by composing with linear forms on ( W ).
  • The transpose retains linearity and has the same rank as the original mapping.

Annihilator of a Subspace

  • The annihilator ( M^\circ ) of a subspace ( M \subset V ) consists of linear forms that evaluate to zero on ( M ).
  • The dimension of the annihilator is given by ( \text{dim } M^\circ = \text{dim } V - \text{dim } M ).

Properties of the Transpose

  • The kernel of the transpose ( T' ) is the annihilator of the image ( T(V) ).

Theorems Relating to Linear Mappings

  • If ( T: V \to W ) is a linear mapping:
    • ( T ) is surjective if and only if ( T' ) is injective.
    • ( T ) is injective if and only if ( T' ) is surjective.

Bidual and Canonical Embedding

  • The bidual of a space ( V ) is defined as ( (V')' = V'' ).
  • The canonical embedding relates each vector in ( V ) to its evaluation mapping in the dual space.

Importance of the Canonical Embedding

  • The canonical embedding establishes an isomorphism between ( V ) and ( V'' ).
  • This mapping is linear, surjective, and injective, confirming that ( \text{Ker } S = {\theta} ).

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